Verify with Energy Atlas: monthly consumption at neighborhood level in 2016.
Neighborhood geometry shapefile is from an email from Eric Daniel Fournier.
## Reading layer `neighborhoods' from data source
## `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/energyAtlas/Neighborhood/neighborhoods/neighborhoods.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 476 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -306628 ymin: -604427.3 xmax: 289125.5 ymax: 97257.92
## CRS: 3310
The following is a preview of the neighborhood data.
## Simple feature collection with 6 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -201772.9 ymin: -565384.3 xmax: 275665.5 ymax: -43132.43
## CRS: 3310
## neighborho pop2014 med_income owned_unit rent_units total_unit pop_sqmi
## 1 39 NA NA NA NA NA NA
## 2 251 7194 144250 2308 294 2602 1886.2738
## 3 195 3868 107475 1192 95 1287 252.3039
## 4 106 50335 90000 11841 5907 17748 3301.7585
## 5 452 33529 142998 7455 5337 12792 1688.3076
## 6 48 NA NA NA NA NA NA
## name pct_own pct_rent geometry
## 1 scripps miramar ranch NA NA MULTIPOLYGON (((275072.1 -5...
## 2 rolling hills estates 88.70100 11.299001 MULTIPOLYGON (((152638.6 -4...
## 3 leona valley 92.61849 7.381507 MULTIPOLYGON (((151115.6 -3...
## 4 chatsworth 66.71738 33.282623 MULTIPOLYGON (((130208.8 -4...
## 5 foster city 58.27861 41.721388 MULTIPOLYGON (((-199586.1 -...
## 6 ncfua subarea ii NA NA MULTIPOLYGON (((259959.2 -5...
The following is a summary of the neighborhood shapefile data. The “neighborho” column is used in matching the neighborhood geometry with Energy Atlas neighborhood level energy.
## neighborho pop2014 med_income owned_unit
## 1 : 1 Min. : 0 Min. : 15532 Min. : 0
## 2 : 1 1st Qu.: 11308 1st Qu.: 58097 1st Qu.: 1949
## 3 : 1 Median : 25588 Median : 82978 Median : 3985
## 4 : 1 Mean : 42468 Mean : 90125 Mean : 7252
## 5 : 1 3rd Qu.: 53953 3rd Qu.:110782 3rd Qu.: 8772
## 6 : 1 Max. :992078 Max. :250001 Max. :176533
## (Other):470 NA's :55 NA's :62 NA's :55
## rent_units total_unit pop_sqmi name
## Min. : 0 Min. : 0 Min. : 0 military facilities: 3
## 1st Qu.: 1237 1st Qu.: 3919 1st Qu.: 2460 san jose : 3
## Median : 4201 Median : 9136 Median : 6372 brentwood : 2
## Mean : 7106 Mean : 14357 Mean : 16512 chinatown : 2
## 3rd Qu.: 8636 3rd Qu.: 17668 3rd Qu.: 12251 downtown : 2
## Max. :132663 Max. :309196 Max. :3006699 fairfax : 2
## NA's :55 NA's :55 NA's :55 (Other) :462
## pct_own pct_rent
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 40.48 1st Qu.: 30.05
## Median : 55.60 Median : 44.40
## Mean : 54.29 Mean : 45.71
## 3rd Qu.: 69.95 3rd Qu.: 59.52
## Max. :100.00 Max. :100.00
## NA's :58 NA's :58
Following is the neighborhood geometry restricted within the boundary of LA county. This is the area we’ll analyze.
## Reading layer `la-county-boundary' from data source
## `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/domain/la-county-boundary.geojson'
## using driver `GeoJSON'
## Simple feature collection with 7 features and 17 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.9446 ymin: 32.79521 xmax: -117.6464 ymax: 34.8233
## CRS: 4326
Join building to neighborhood by first computing the building centroids, and check which neighborhood polygon contains the centroid.
The following is a preview of the matching result data frame
| OBJECTID | neighborho |
|---|---|
| 19 | 78 |
| 20 | 78 |
| 21 | 78 |
| 23 | 78 |
| 24 | 78 |
| 25 | 78 |
The following is a summary of the matching
| min | Q1 | median | mean | Q3 | max |
|---|---|---|---|---|---|
| 2 | 1514.75 | 3505.5 | 5354.216 | 6828.25 | 67309 |
Read neighborhood level annual energy data, usage_bld_kwh.csv, downloaded from the Energy Atlas website, https://ucla.app.box.com/s/z2i515cc2lgn3t6rpwe1ymcqygr4y5a0. Different from the Dropbox data, the new data has “masked” and NA’s in the usage column instead of the -7777, -8888, -9999 code. The “all” category is also removed in the new data set. “id” column is renamed to “geo_id”. The following is a preview of the usage_bld_kwh.csv
| geo_id | sqft | usage | usage_med | usage_med_sqft | usetype | year | name | solar_potential | pop | usage_percap |
|---|---|---|---|---|---|---|---|---|---|---|
| cities_1 | 1253 | masked | masked | masked | agriculture | 2016 | agoura hills | NA | NA | masked |
| cities_1 | 4802115 | 65825608.0000 | 121901 | 9.7676 | commercial | 2016 | agoura hills | NA | NA | NA |
| cities_1 | 8906397 | 43707916.6000 | 9136 | 3.9839 | condo | 2016 | agoura hills | NA | NA | NA |
| cities_1 | 734698 | masked | masked | masked | industrial | 2016 | agoura hills | NA | NA | masked |
| cities_1 | 100192 | masked | masked | masked | institutional | 2016 | agoura hills | NA | NA | masked |
| cities_1 | 742420 | 2010646.5000 | 70823 | 3.2102 | multi_family | 2016 | agoura hills | NA | NA | NA |
Join Energy Atlas energy data with neighborhood geometry * First split the “geo_id” column by the “_” * Filtering out the id’s at neighborhood level (prefix of “geo_id” == “neighborhoods”) * Join the energy and shapefile data with the numeric suffix of “geo_id” and the “neighborho” column in the shapefile
Filter the data by four steps. The following table shows the number of neighborhoods and records left after each step. The last step is meant to calculate the total usage for a neighborhood. As is shown here, the building types in EnergyAtlas overlaps. In order to compute the total of a neighborhood, we need to keep only the non-overlapping usetyeps. The definition of each use type are as following according to https://energyatlas.ucla.edu/methods
| usetype | definition |
|---|---|
| condo | Condominiums |
| multi_family | Duplexes to large apartment complexes. |
| res_total | Sum of all residential categories. |
| single_family | NA |
| residential_other | Mobile home parks, manufactured homes, nursing homes, rural residential, and unknown other residential use codes that do not clearly fit within single family, multi-family, or condominium categories. |
| commercial | Office buildings, hotels, retail, restaurants, mixed-use commercial, etc. |
| industrial | Manufacturing, warehouses, processing facilities, extraction sites, etc. |
| institutional | Schools, public hospitals, government owned facilities, churches, tax-exempt properties, etc. |
| agriculture | Farms, agricultural lands, orchards, etc. |
| other | Spans diverse range of use types unable to fit within the pre-set categories, including miscellaneous bus terminals, airports, vacant land, reservoirs, truck terminals, right-of-ways, etc. |
| Filtering Steps | Number of Neighborhoods | Number of Records |
|---|---|---|
| Original data | 272 | 3264 |
| Restrict to within LA county | 263 | 3156 |
| Remove masked data | 248 | 1358 |
| Keep records with positive sqft | 248 | 1244 |
| Remove agriculture and “other” usetype | 248 | 1220 |
| Restrict to the major usetypes | 248 | 523 |
Following is a preview of the aggregated Energy Atlas data by neighborhood and by neighborhood and usetype
| id.num | usage | m2 | data.source |
|---|---|---|---|
| 64 | 26652610 | 449924.7 | Energy Atlas 2016 |
| 65 | 29456039 | 554136.2 | Energy Atlas 2016 |
| 66 | 138283531 | 1801069.3 | Energy Atlas 2016 |
| 67 | 15448257 | 236423.9 | Energy Atlas 2016 |
| 68 | 313064947 | 3629159.2 | Energy Atlas 2016 |
| 69 | 11983734 | 259334.2 | Energy Atlas 2016 |
| id.num | usetype | usage | m2 | data.source |
|---|---|---|---|---|
| 100 | res_total | 50189240 | 951539.4 | Energy Atlas 2016 |
| 101 | res_total | 91580166 | 1562195.4 | Energy Atlas 2016 |
| 102 | commercial | 19134101 | 107975.5 | Energy Atlas 2016 |
| 102 | industrial | 54522983 | 605488.6 | Energy Atlas 2016 |
| 102 | res_total | 31257106 | 689366.6 | Energy Atlas 2016 |
| 103 | commercial | 163046619 | 1183233.4 | Energy Atlas 2016 |
Simulation results are saved in a csv file: annual_sim_result_by_idf_epw.csv
First aggregate simulation results to annual total, and convert the consumption from J to kwh.
Then map building types in the simulation data set to the EnergyAtlas types. Note that nursing homes are matched to residential rather than institutional. The following table shows the mapping from EnergyPlus models to EnergyAtlas types
| EnergyAtlas | simulation |
|---|---|
| commercial | FullServiceRestaurant |
| LargeHotel | |
| LargeOffice | |
| MediumOffice | |
| RetailStandalone | |
| SmallHotel | |
| SmallOffice | |
| SuperMarket | |
| industrial | HeavyManufacturing |
| LightManufacturing | |
| Warehouse | |
| institutional | Hospital |
| NursingHome_baseline | |
| PrimarySchool | |
| Religious | |
| SecondarySchool | |
| res_total | MidriseApartment |
| MultiFamily | |
| SingleFamily |
Following is a preview of the simulation results aggregated to neighborhood level
| neighborho | energy.kwh | building.area.m2 | FootprintArea.m2 |
|---|---|---|---|
| 64 | 7981954 | 53632.38 | 83553.09 |
| 65 | 120638671 | 447717.48 | 355309.44 |
| 66 | 176018035 | 1233231.33 | 1345776.95 |
| 67 | 3140236 | 22189.72 | 35505.44 |
| 68 | 915393216 | 2800466.98 | 2733022.22 |
| 69 | 41709827 | 211119.03 | 253185.40 |
| neighborho | usetype | energy.kwh | building.area.m2 | FootprintArea.m2 |
|---|---|---|---|---|
| 64 | industrial | 641908.2 | 359.7088 | 529.0762 |
| 64 | institutional | 208216.9 | 631.3484 | 660.7435 |
| 64 | res_total | 7131829.3 | 52641.3205 | 82363.2751 |
| 65 | commercial | 21772060.1 | 21097.8687 | 9586.2425 |
| 65 | industrial | 5028840.1 | 5132.3534 | 4845.6390 |
| 65 | institutional | 8738001.4 | 10683.0355 | 11564.4421 |
## Reading layer `grid_with_building' from data source
## `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/grid_with_building.geojson'
## using driver `GeoJSON'
## Simple feature collection with 62 features and 3 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -118.885 ymin: 33.24275 xmax: -117.5225 ymax: 34.707
## CRS: 4326
For most neighborhoods, the total building area recorded in Energy Atlas is larger than the area recorded in the simulation data set (building characteristics source data is from “Assessor_Parcels_Data_-_2019.csv” joined to the building geometry from LARIAC6_LA_County.geojson)
The following compares the building total sqft of the four major usetypes.
For most neighborhoods, the total building area recorded in Energy Atlas for each of the four major usetypes is larger than the area recorded in the simulation data set.
The following plots the difference in the percentage of each four usetypes in a neighborhood. We can see that simulation data sets have higher percentage of residential and industrial types and lower ratio of commercial buildings in most neighborhoods, compared against Energy Atlas data.
The following shows the total energy usage and usage per total area comparison.